Type
ArticleKAUST Department
Applied Mathematics and Computational Science ProgramComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Environmental Statistics Group
Extreme Computing Research Center
Office of the President
Spatio-Temporal Statistics and Data Analysis Group
Statistics Program
Date
2021-07-08Online Publication Date
2021-07-08Print Publication Date
2021-12Embargo End Date
2022-07-08Submitted Date
2021-04-20Permanent link to this record
http://hdl.handle.net/10754/670104
Metadata
Show full item recordAbstract
As spatial datasets are becoming increasingly large and unwieldy, exact inference on spatial models becomes computationally prohibitive. Various approximation methods have been proposed to reduce the computational burden. Although comprehensive reviews on these approximation methods exist, comparisons of their performances are limited to small and medium sizes of datasets for a few selected methods. To achieve a comprehensive comparison comprising as many methods as possible, we organized the Competition on Spatial Statistics for Large Datasets. This competition had the following novel features: (1) we generated synthetic datasets with the ExaGeoStat software so that the number of generated realizations ranged from 100 thousand to 1 million; (2) we systematically designed the data-generating models to represent spatial processes with a wide range of statistical properties for both Gaussian and non-Gaussian cases; (3) the competition tasks included both estimation and prediction, and the results were assessed by multiple criteria; and (4) we have made all the datasets and competition results publicly available to serve as a benchmark for other approximation methods. In this paper, we disclose all the competition details and results along with some analysis of the competition outcomes.Citation
Huang, H., Abdulah, S., Sun, Y., Ltaief, H., Keyes, D. E., & Genton, M. G. (2021). Competition on Spatial Statistics for Large Datasets. Journal of Agricultural, Biological and Environmental Statistics. doi:10.1007/s13253-021-00457-zSponsors
Funding was provided by King Abdullah University of Science and Technology.Publisher
Springer Science and Business Media LLCAdditional Links
https://link.springer.com/10.1007/s13253-021-00457-zae974a485f413a2113503eed53cd6c53
10.1007/s13253-021-00457-z